source("002-LoadData.R")

library(stats)
library(forcats)
library(effsize)
library(clinfun)
library(ggplot2)
library(reshape2)
library(forcats)

#newcomer recurring, non recurring
print("Newcomers over time:")
## [1] "Newcomers over time:"
timelyAgg <- aggregate(newcomers$quantity[order(fct_rev(newcomers$timeRange))],
                       by=list(newcomers$Mapperid[order(fct_rev(newcomers$timeRange))],
                               newcomers$Happeningid[order(fct_rev(newcomers$timeRange))],
                               newcomers$HappeningType[order(fct_rev(newcomers$timeRange))]),
                       FUN=cumsum)

newcomersOverTime <- aggregate( timelyAgg$x>0, by=list(timelyAgg$Group.3) , FUN=sum)
newcomersOverTime$during <- summary(nonrecurringNewcomers$HappeningType)+newcomersOverTime$V4

1-newcomersOverTime[,-1]/newcomersOverTime[,6]
##          V1        V2        V3        V4 during
## 1 0.8817204 0.8207885 0.7670251 0.7311828      0
## 2 0.9342105 0.8815789 0.7631579 0.5657895      0
## 3 0.9018692 0.8411215 0.7757009 0.6028037      0
newcomerPlot <- ggplot(melt(newcomersOverTime),aes(x=rev(variable),group=Group.1,color=Group.1,y=value))+
  geom_line()+
  theme_minimal()+ 
  theme(axis.text.x = element_text(angle = 45, vjust = 1,size = 10, hjust = 1),
        axis.text.y=element_text(size=10))+
  scale_x_discrete(labels=c("during","one month","six months","one year","two years"))+
  scale_colour_manual(values=c("#8c510a","#01665e","#5ab4ac"))+
  labs(x = "Time interval of last contribution",y="Nr. of newcomers contributing",color="Happening type")
## Using Group.1 as id variables
print(newcomerPlot)

ggsave(path="/home/moritz/Schreibtisch/Masterarbeit/02_Text/figures",
       filename = "newcomerPlot.eps",
       plot=newcomerPlot,
       units="cm",
       width = 16,
       height = 12)

print("for recurring newcomers only: ")
## [1] "for recurring newcomers only: "
digiArea <- c("element_density","tag_density","user_density","area_diversity")
contribIndepVar <- c("quantity","discussion_size","notes_size")

## statistics on newcomers
for (variableName in names(dependentVnewcomer.changes)){
  print("")
  print("========================================================================")
  print(paste("RESULTS FOR VARIABLE",variableName))
  print("========================================================================")
  print("")
  print("")
  
  jitterplot <- ggplot(dependentVnewcomer.changes,aes(x=independentVnewcomer.mappers$timeRange,
                                                      y=dependentVnewcomer.changes[,variableName]))+
    geom_count(position="jitter",aes(color=independentVnewcomer.mappers$HappeningType))+
    labs(title=variableName)+
    scale_color_manual(values=c("#8c510a","#01665e","#5ab4ac"))+
    theme(legend.title = element_blank(),
          axis.title.x = element_blank(),
          axis.title.y=element_blank())+
    scale_y_continuous(trans='pseudo_log')
  print(jitterplot)
  
  print("")
  
  lineplot <- ggplot(dependentVnewcomer.changes,aes(x=independentVnewcomer.mappers$timeRange,
                                                    group=interaction(independentVnewcomer.mappers$Mapperid,independentVnewcomer.mappers$Happeningid),
                                                    y=dependentVnewcomer.changes[,variableName]))+
    geom_line(aes(color=independentVnewcomer.mappers$HappeningType))+
    labs(title=variableName)+
    scale_color_manual(values=c("#8c510a","#01665e","#5ab4ac"))+
    theme(legend.title = element_blank(),
          axis.title.x = element_blank(),
          axis.title.y=element_blank())+
    scale_y_continuous(trans='pseudo_log')
  print(lineplot)
  
  print("")
  print("Boxplot on contributors only: ")
  
  boxplot <- ggplot(dependentVnewcomer.changes[dependentVnewcomer.changes$quantity>0,],
                    aes(x=independentVnewcomer.mappers[dependentVnewcomer.changes$quantity>0,"timeRange"],
                        color=independentVnewcomer.mappers[dependentVnewcomer.changes$quantity>0,"HappeningType"],
                        y=dependentVnewcomer.changes[dependentVnewcomer.changes$quantity>0,variableName]))+
    geom_boxplot()+
    labs(title=variableName)+
    scale_color_manual(values=c("#8c510a","#01665e","#5ab4ac"))+
    theme(legend.title = element_blank(),
          axis.title.x = element_blank(),
          axis.title.y=element_blank())+
    scale_y_continuous(trans='pseudo_log')
  print(boxplot)
  
  print("")
  
  
  for(i in levels(independentVnewcomer.mappers$timeRange)){
    
    #skip forbidden variables
    if(i=="one month"&any(digiArea==variableName)){
      next
    }
    
    #get data for time interaval and variable
    if(any(contribIndepVar==variableName)){
      variable <- dependentVnewcomer.changes[independentVnewcomer.mappers$timeRange==i,variableName]
      classes <- independentVnewcomer.mappers[independentVnewcomer.mappers$timeRange==i,"HappeningType"]
    }else{
      variable <- dependentVnewcomer.changes[independentVnewcomer.mappers$timeRange==i&dependentVnewcomer.changes$quantity>0,variableName]
      classes <- independentVnewcomer.mappers[independentVnewcomer.mappers$timeRange==i&dependentVnewcomer.changes$quantity>0,"HappeningType"]
    }

    
    print("")
    print(paste("Time frame: ",i))
    print("")
    print("N:")
    print(summary(classes))
    print("")
    print(paste("Summary Statistics for variable ",variableName))
    print(aggregate(variable,by=list(classes),FUN=summary))
    print("")
    
    #kruskal test
    kruskal <- kruskal.test(x=variable,g=classes)
    if(!is.nan(kruskal$p.value) & kruskal$p.value<=0.05){

      print("")
      print(paste(variableName,"is significantly influenced by an event for",i))
      print("")
      print("")
      
      # wilcox pariwise post hoc
      #http://www.sthda.com/english/wiki/kruskal-wallis-test-in-r
      wilcox <- pairwise.wilcox.test(x=variable,g=classes,p.adjust.method = "BH")
      print("Pairwise comparison: ")
      print(wilcox$p.value)
      
      
      # mode IVs for CFM
      if(wilcox$p.value["CFM","CG"]<=0.05){
        print(paste("Cohends d for effect size of the CFM on",variableName,":"))
        print(cohen.d(d=variable[classes!="CRM"],f=fct_drop(classes[classes!="CRM"])))
        print("")
        
        if(any(contribIndepVar==variableName)){
          variable1 <- dependentVnewcomer.changes[independentVnewcomer.mappers$timeRange==i&
                                                    independentVnewcomer.mappers$HappeningType=="CFM",variableName]
          # economic status
            classes1 <- independentVnewcomer.mappers[independentVnewcomer.mappers$timeRange==i&
                                                       independentVnewcomer.mappers$HappeningType=="CFM","economic_status"]
        }else{
          variable1 <- dependentVnewcomer.changes[independentVnewcomer.mappers$timeRange==i&
                                                    dependentVnewcomer.changes$quantity>0&
                                                    independentVnewcomer.mappers$HappeningType=="CFM",variableName]
          # economic status
          classes1 <- independentVnewcomer.mappers[independentVnewcomer.mappers$timeRange==i&
                                                     dependentVnewcomer.changes$quantity>0&
                                                     independentVnewcomer.mappers$HappeningType=="CFM","economic_status"]
        }
        
        
          jockey <- jonckheere.test(variable1,classes1,nperm=1000)
          print("N:")
          print(summary(classes1))
          print("")
          print("Summary Statistics: ")
          print(aggregate(variable1,by=list(classes1),FUN=summary))
          print("")
        if(jockey$p.value<=0.05){
          wilcox2 <- pairwise.wilcox.test(x=variable1,g=classes1,p.adjust.method = "BH")
          if(any(wilcox2$p.value<=0.05,na.rm=TRUE)){
            print("Pairwise comparison: ")
            print(wilcox2$p.value)
            print(paste("Analyses of the effect of the economic status for CFM with a p-value of",jockey$p.value))
            { sink("/dev/null"); print(plot(x=classes1,
                                            y=variable1,
                                            main="The economic status has an effect",
                                            sub=paste("on this variable (",variableName,") for CFM"))); 
              sink(); }
            print(plot.new())
            print("")
          }
        }
        
        #culture
          if(any(contribIndepVar==variableName)){
          classes1 <- independentVnewcomer.mappers[independentVnewcomer.mappers$timeRange==i&
                                                   independentVnewcomer.mappers$HappeningType=="CFM","culture"]
          }else{
            classes1 <- independentVnewcomer.mappers[independentVnewcomer.mappers$timeRange==i&
                                                       dependentVnewcomer.changes$quantity>0&
                                                       independentVnewcomer.mappers$HappeningType=="CFM","culture"]
          }
        classes1 <- fct_drop(fct_lump_min(classes1,min=10))

        print("N:")
        print(summary(classes1))
        print("")
        print("Summary Statistics: ")
        print(aggregate(variable1,by=list(classes1),FUN=summary))
        print("")
        
        if(length(levels(classes1))>1){
          kruskal2 <- kruskal.test(x=variable1,g=classes1)
          if(!is.nan(kruskal2$p.value) & kruskal2$p.value<=0.05){
            wilcox2 <- pairwise.wilcox.test(x=variable1,g=classes1,p.adjust.method = "BH")
            if(any(wilcox2$p.value<=0.05,na.rm=TRUE)){
              print("")
              print(paste(variableName,"is significantly influenced by at least one culture for CFM with p=",kruskal2$p.value))
              print("")
              print("Pairwise comparison: ")
              print(wilcox2$p.value)
              { sink("/dev/null"); print(plot(classes1,variable1,main="The culture has an effect",
                                              sub=paste("on this variable (",variableName,") for CFM"))); sink(); }
              print(plot.new())
              print("")
            }
          }
        }
        
        
        
        
      }
      
      # other IVs for CRM
      if(wilcox$p.value["CRM","CG"]<=0.05){
        print(paste("Cohends d for effect size of the CRM on",variableName,":"))
        print(cohen.d(d=variable[classes!="CFM"],f=fct_drop(classes[classes!="CFM"])))
        print("")
        
        if(any(contribIndepVar==variableName)){
        variable2 <- dependentVnewcomer.changes[independentVnewcomer.mappers$timeRange==i&
                                                  independentVnewcomer.mappers$HappeningType=="CRM",variableName]
        
        ## distance during event
        classes2 <- independentVnewcomer.mappers[independentVnewcomer.mappers$timeRange==i&
                                                   independentVnewcomer.mappers$HappeningType=="CRM","event_mapping_distance"]
        }else{
          variable2 <- dependentVnewcomer.changes[independentVnewcomer.mappers$timeRange==i&
                                                    dependentVnewcomer.changes$quantity>0&
                                                    independentVnewcomer.mappers$HappeningType=="CRM",variableName]
          
          ## distance during event
          classes2 <- independentVnewcomer.mappers[independentVnewcomer.mappers$timeRange==i&
                                                     dependentVnewcomer.changes$quantity>0&
                                                     independentVnewcomer.mappers$HappeningType=="CRM","event_mapping_distance"]
        }
        print("N:")
        print(length(classes2))
        print("")
        model <- lm(variable2~classes2)
        if(any(summary(model)$coefficients[2,4]<=0.05,na.rm = TRUE)){
          print("Analyses of the effect of the distance to the Region mapped during the event for CRM")
          { sink("/dev/null"); print(plot(classes2,variable2,log="x",main="The event mapping distance has an effect",
                                          sub=paste("on this variable (",variableName,") for CRM"))); sink(); }
          print(abline(model))
          print(plot.new())
          print(summary(model))
          print("")
        }
      }
      
      # between happenings
      if(!is.nan(wilcox$p.value["CRM","CFM"]) & wilcox$p.value["CRM","CFM"]<=0.05){
        print(paste("Cohends d for effect size between CRM and CFM on",variableName,":"))
        print(cohen.d(d=variable[classes!="CG"],f=fct_drop(classes[classes!="CG"])))
        print("")
      }
    }
    print("")
    print("")
    print("--------------------------------------------------------------------------------")
    print("")
    print("")
  }
}
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE quantity"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame:  one month"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  75  33  85 
## [1] ""
## [1] "Summary Statistics for variable  quantity"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG    0.00000    0.00000    0.00000  106.88000    1.00000
## 2     CFM    0.00000    1.00000   10.00000   87.60606   66.00000
## 3     CRM    0.00000    0.00000   14.00000  102.36471   84.00000
##       x.Max.
## 1 5621.00000
## 2 1628.00000
## 3 2021.00000
## [1] ""
## [1] ""
## [1] "quantity is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## [1] "Pairwise comparison: "
##               CG       CFM
## CFM 6.700544e-06        NA
## CRM 7.499466e-07 0.9007524
## [1] "Cohends d for effect size of the CFM on quantity :"
## 
## Cohen's d
## 
## d estimate: 0.03301285 (negligible)
## 95 percent confidence interval:
##      lower      upper 
## -0.3811621  0.4471879 
## [1] ""
## [1] "N:"
##          low income lower middle income upper middle income 
##                   2                   4                  13 
##         high income 
##                  14 
## [1] ""
## [1] "Summary Statistics: "
##               Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.
## 1          low income   10.0000   10.0000   10.0000   10.0000   10.0000
## 2 lower middle income    0.0000    0.0000    0.0000   34.7500   34.7500
## 3 upper middle income    0.0000    3.0000   34.0000  167.6154  104.0000
## 4         high income    0.0000    0.2500    3.0000   39.5000   43.7500
##      x.Max.
## 1   10.0000
## 2  139.0000
## 3 1628.0000
## 4  323.0000
## [1] ""
## [1] "N:"
## Latin  American           Other 
##              20              13 
## [1] ""
## [1] "Summary Statistics: "
##           Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.
## 1 Latin  American    0.0000    1.5000   12.5000   34.7500   68.7500
## 2           Other    0.0000    1.0000   10.0000  168.9231   51.0000
##      x.Max.
## 1  111.0000
## 2 1628.0000
## [1] ""
## [1] "Cohends d for effect size of the CRM on quantity :"
## 
## Cohen's d
## 
## d estimate: 0.009065739 (negligible)
## 95 percent confidence interval:
##      lower      upper 
## -0.3038371  0.3219686 
## [1] ""
## [1] "N:"
## [1] 85
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  75  33  85 
## [1] ""
## [1] "Summary Statistics for variable  quantity"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG    0.00000    0.00000    0.00000  184.38667    4.50000
## 2     CFM    0.00000    0.00000    0.00000   51.72727   32.00000
## 3     CRM    0.00000    0.00000    0.00000   59.67059   23.00000
##       x.Max.
## 1 5427.00000
## 2  604.00000
## 3 1459.00000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  75  33  85 
## [1] ""
## [1] "Summary Statistics for variable  quantity"
##   Group.1       x.Min.    x.1st Qu.     x.Median       x.Mean    x.3rd Qu.
## 1      CG     0.000000     0.000000     0.000000   309.920000     6.500000
## 2     CFM     0.000000     0.000000     0.000000     8.575758     0.000000
## 3     CRM     0.000000     0.000000     0.000000    44.929412     0.000000
##         x.Max.
## 1 21582.000000
## 2   208.000000
## 3  1847.000000
## [1] ""
## [1] ""
## [1] "quantity is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## [1] "Pairwise comparison: "
##             CG       CFM
## CFM 0.02043907        NA
## CRM 0.12334534 0.2015395
## [1] "Cohends d for effect size of the CFM on quantity :"
## 
## Cohen's d
## 
## d estimate: 0.144717 (negligible)
## 95 percent confidence interval:
##      lower      upper 
## -0.2698939  0.5593280 
## [1] ""
## [1] "N:"
##          low income lower middle income upper middle income 
##                   2                   4                  13 
##         high income 
##                  14 
## [1] ""
## [1] "Summary Statistics: "
##               Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean
## 1          low income   0.000000   0.000000   0.000000   0.000000
## 2 lower middle income   0.000000   0.000000   0.000000   0.000000
## 3 upper middle income   0.000000   0.000000   0.000000  16.000000
## 4         high income   0.000000   0.000000   0.000000   5.357143
##    x.3rd Qu.     x.Max.
## 1   0.000000   0.000000
## 2   0.000000   0.000000
## 3   0.000000 208.000000
## 4   1.500000  63.000000
## [1] ""
## [1] "N:"
## Latin  American           Other 
##              20              13 
## [1] ""
## [1] "Summary Statistics: "
##           Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.
## 1 Latin  American   0.00000   0.00000   0.00000   0.15000   0.00000
## 2           Other   0.00000   0.00000   0.00000  21.53846   2.00000
##      x.Max.
## 1   3.00000
## 2 208.00000
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] ""
## [1] "quantity is significantly influenced by at least one culture for CFM with p= 0.0416451593124405"
## [1] ""
## [1] "Pairwise comparison: "
##       Latin  American
## Other      0.04469366

## NULL
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  75  33  85 
## [1] ""
## [1] "Summary Statistics for variable  quantity"
##   Group.1      x.Min.   x.1st Qu.    x.Median      x.Mean   x.3rd Qu.
## 1      CG     0.00000     0.00000     0.00000   151.70667     4.50000
## 2     CFM     0.00000     0.00000     0.00000    29.84848     0.00000
## 3     CRM     0.00000     0.00000     0.00000    51.51765     0.00000
##        x.Max.
## 1 10847.00000
## 2   362.00000
## 3  1677.00000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE creations_share"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame:  one month"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  26  25  57 
## [1] ""
## [1] "Summary Statistics for variable  creations_share"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.0000000 0.2708333 0.6252637 0.5946545 1.0000000 1.0000000
## 2     CFM 0.0000000 0.2662539 0.3809524 0.4614714 0.7209302 1.0000000
## 3     CRM 0.1538462 0.8947368 0.9731544 0.9053623 1.0000000 1.0000000
## [1] ""
## [1] ""
## [1] "creations_share is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##              CG          CFM
## CFM 0.183916263           NA
## CRM 0.005294576 9.424154e-07
## [1] "Cohends d for effect size of the CRM on creations_share :"
## 
## Cohen's d
## 
## d estimate: -1.206611 (large)
## 95 percent confidence interval:
##      lower      upper 
## -1.7130086 -0.7002138 
## [1] ""
## [1] "N:"
## [1] 57
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on creations_share :"
## 
## Cohen's d
## 
## d estimate: -2.001366 (large)
## 95 percent confidence interval:
##     lower     upper 
## -2.571120 -1.431611 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  36  14  27 
## [1] ""
## [1] "Summary Statistics for variable  creations_share"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.0000000 0.2465004 0.6007812 0.5902017 0.8957787 1.0000000
## 2     CFM 0.1428571 0.3750000 0.5832896 0.5290579 0.6359890 0.9200000
## 3     CRM 0.5600000 0.7718988 0.9130435 0.8602845 0.9730264 1.0000000
## [1] ""
## [1] ""
## [1] "creations_share is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##             CG          CFM
## CFM 0.53672735           NA
## CRM 0.01464143 7.300159e-05
## [1] "Cohends d for effect size of the CRM on creations_share :"
## 
## Cohen's d
## 
## d estimate: -0.9515564 (large)
## 95 percent confidence interval:
##      lower      upper 
## -1.4881154 -0.4149973 
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on creations_share :"
## 
## Cohen's d
## 
## d estimate: -1.938248 (large)
## 95 percent confidence interval:
##     lower     upper 
## -2.732731 -1.143764 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  creations_share"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.0000000 0.4000000 0.5913724 0.5610299 0.8333333 1.0000000
## 2     CFM 0.0000000 0.0000000 0.3333333 0.4056777 0.7142857 0.9807692
## 3     CRM 0.4000000 0.8791209 0.9583333 0.8744286 1.0000000 1.0000000
## [1] ""
## [1] ""
## [1] "creations_share is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] "Pairwise comparison: "
##             CG        CFM
## CFM 0.36237664         NA
## CRM 0.00168505 0.03904341
## [1] "Cohends d for effect size of the CRM on creations_share :"
## 
## Cohen's d
## 
## d estimate: -1.118202 (large)
## 95 percent confidence interval:
##      lower      upper 
## -1.7185207 -0.5178841 
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on creations_share :"
## 
## Cohen's d
## 
## d estimate: -1.908229 (large)
## 95 percent confidence interval:
##      lower      upper 
## -3.0714421 -0.7450163 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  creations_share"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.0000000 0.2500000 0.5000000 0.5336449 0.9230769 1.0000000
## 2     CFM 0.2500000 0.5333333 0.9382716 0.7293129 0.9502762 0.9746835
## 3     CRM 0.0000000 0.7204301 0.7857143 0.7650795 0.9250000 1.0000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE tag_changes_share"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame:  one month"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  26  25  57 
## [1] ""
## [1] "Summary Statistics for variable  tag_changes_share"
##   Group.1      x.Min.   x.1st Qu.    x.Median      x.Mean   x.3rd Qu.
## 1      CG 0.000000000 0.000000000 0.107044868 0.304370109 0.500000000
## 2     CFM 0.000000000 0.002457002 0.267441860 0.293917820 0.486486486
## 3     CRM 0.000000000 0.000000000 0.000000000 0.018927889 0.005802708
##        x.Max.
## 1 1.000000000
## 2 1.000000000
## 3 0.418604651
## [1] ""
## [1] ""
## [1] "tag_changes_share is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##               CG         CFM
## CFM 0.5653263389          NA
## CRM 0.0003040143 1.28492e-06
## [1] "Cohends d for effect size of the CRM on tag_changes_share :"
## 
## Cohen's d
## 
## d estimate: 1.263038 (large)
## 95 percent confidence interval:
##     lower     upper 
## 0.7533697 1.7727062 
## [1] ""
## [1] "N:"
## [1] 57
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on tag_changes_share :"
## 
## Cohen's d
## 
## d estimate: 1.562976 (large)
## 95 percent confidence interval:
##    lower    upper 
## 1.027359 2.098594 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  36  14  27 
## [1] ""
## [1] "Summary Statistics for variable  tag_changes_share"
##   Group.1      x.Min.   x.1st Qu.    x.Median      x.Mean   x.3rd Qu.
## 1      CG 0.000000000 0.000000000 0.171750663 0.295323781 0.500000000
## 2     CFM 0.000000000 0.026345178 0.179842715 0.256703767 0.450892857
## 3     CRM 0.000000000 0.000000000 0.005483208 0.048957437 0.097169811
##        x.Max.
## 1 1.000000000
## 2 0.857142857
## 3 0.229787234
## [1] ""
## [1] ""
## [1] "tag_changes_share is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##             CG        CFM
## CFM 0.92162630         NA
## CRM 0.01065215 0.01065215
## [1] "Cohends d for effect size of the CRM on tag_changes_share :"
## 
## Cohen's d
## 
## d estimate: 0.9498737 (large)
## 95 percent confidence interval:
##     lower     upper 
## 0.4134092 1.4863381 
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on tag_changes_share :"
## 
## Cohen's d
## 
## d estimate: 1.256921 (large)
## 95 percent confidence interval:
##     lower     upper 
## 0.5340187 1.9798230 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  tag_changes_share"
##   Group.1      x.Min.   x.1st Qu.    x.Median      x.Mean   x.3rd Qu.
## 1      CG 0.000000000 0.066666667 0.142857143 0.316311355 0.484848485
## 2     CFM 0.000000000 0.095238095 0.142857143 0.380952381 0.666666667
## 3     CRM 0.000000000 0.000000000 0.000000000 0.037592050 0.004385965
##        x.Max.
## 1 1.000000000
## 2 1.000000000
## 3 0.500000000
## [1] ""
## [1] ""
## [1] "tag_changes_share is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##               CG        CFM
## CFM 8.114304e-01         NA
## CRM 4.340506e-05 0.01560836
## [1] "Cohends d for effect size of the CRM on tag_changes_share :"
## 
## Cohen's d
## 
## d estimate: 0.98351 (large)
## 95 percent confidence interval:
##     lower     upper 
## 0.3920478 1.5749723 
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on tag_changes_share :"
## 
## Cohen's d
## 
## d estimate: 1.672336 (large)
## 95 percent confidence interval:
##     lower     upper 
## 0.5392536 2.8054174 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  tag_changes_share"
##   Group.1      x.Min.   x.1st Qu.    x.Median      x.Mean   x.3rd Qu.
## 1      CG 0.000000000 0.022727273 0.235849057 0.290048252 0.500000000
## 2     CFM 0.000000000 0.004219409 0.018518519 0.221214252 0.333333333
## 3     CRM 0.000000000 0.000000000 0.010733453 0.080153250 0.035087719
##        x.Max.
## 1 1.000000000
## 2 0.750000000
## 3 0.763157895
## [1] ""
## [1] ""
## [1] "tag_changes_share is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] "Pairwise comparison: "
##             CG       CFM
## CFM 0.57151082        NA
## CRM 0.01636417 0.5715108
## [1] "Cohends d for effect size of the CRM on tag_changes_share :"
## 
## Cohen's d
## 
## d estimate: 0.8134778 (large)
## 95 percent confidence interval:
##     lower     upper 
## 0.2317254 1.3952301 
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE geometry_changes_share"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame:  one month"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  26  25  57 
## [1] ""
## [1] "Summary Statistics for variable  geometry_changes_share"
##   Group.1      x.Min.   x.1st Qu.    x.Median      x.Mean   x.3rd Qu.
## 1      CG 0.000000000 0.000000000 0.000000000 0.077093814 0.001734567
## 2     CFM 0.000000000 0.014742015 0.245454545 0.277570292 0.461538462
## 3     CRM 0.000000000 0.000000000 0.026666667 0.080000103 0.093373494
##        x.Max.
## 1 0.666666667
## 2 0.727272727
## 3 0.692307692
## [1] ""
## [1] ""
## [1] "geometry_changes_share is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##              CG         CFM
## CFM 0.001679646          NA
## CRM 0.048452234 0.001871586
## [1] "Cohends d for effect size of the CFM on geometry_changes_share :"
## 
## Cohen's d
## 
## d estimate: -0.9351151 (large)
## 95 percent confidence interval:
##      lower      upper 
## -1.5279721 -0.3422581 
## [1] ""
## [1] "N:"
##          low income lower middle income upper middle income 
##                   2                   1                  12 
##         high income 
##                  10 
## [1] ""
## [1] "Summary Statistics: "
##               Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean
## 1          low income 0.00000000 0.10000000 0.20000000 0.20000000
## 2 lower middle income 0.39568345 0.39568345 0.39568345 0.39568345
## 3 upper middle income 0.00000000 0.23170996 0.42194570 0.38138762
## 4         high income 0.00000000 0.00000000 0.04047406 0.15669224
##    x.3rd Qu.     x.Max.
## 1 0.30000000 0.40000000
## 2 0.39568345 0.39568345
## 3 0.57142857 0.66666667
## 4 0.19251337 0.72727273
## [1] ""
## [1] "N:"
## Latin  American           Other 
##              15              10 
## [1] ""
## [1] "Summary Statistics: "
##           Group.1      x.Min.   x.1st Qu.    x.Median      x.Mean
## 1 Latin  American 0.000000000 0.040474061 0.351351351 0.309523836
## 2           Other 0.000000000 0.003685504 0.188948307 0.229639976
##     x.3rd Qu.      x.Max.
## 1 0.535714286 0.666666667
## 2 0.391963828 0.727272727
## [1] ""
## [1] "Cohends d for effect size of the CRM on geometry_changes_share :"
## 
## Cohen's d
## 
## d estimate: -0.01932379 (negligible)
## 95 percent confidence interval:
##      lower      upper 
## -0.4902017  0.4515541 
## [1] ""
## [1] "N:"
## [1] 57
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on geometry_changes_share :"
## 
## Cohen's d
## 
## d estimate: 1.120944 (large)
## 95 percent confidence interval:
##     lower     upper 
## 0.6127743 1.6291147 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  36  14  27 
## [1] ""
## [1] "Summary Statistics for variable  geometry_changes_share"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG 0.00000000 0.00000000 0.00000000 0.12916112 0.11634846
## 2     CFM 0.02000000 0.11607143 0.31712394 0.27907777 0.39615385
## 3     CRM 0.00000000 0.01959330 0.05263158 0.09921119 0.15194229
##       x.Max.
## 1 0.83333333
## 2 0.58518519
## 3 0.33333333
## [1] ""
## [1] ""
## [1] "geometry_changes_share is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] "Pairwise comparison: "
##              CG         CFM
## CFM 0.001247722          NA
## CRM 0.035260750 0.001247722
## [1] "Cohends d for effect size of the CFM on geometry_changes_share :"
## 
## Cohen's d
## 
## d estimate: -0.6792594 (medium)
## 95 percent confidence interval:
##       lower       upper 
## -1.32710918 -0.03140969 
## [1] ""
## [1] "N:"
##          low income lower middle income upper middle income 
##                   0                   4                   5 
##         high income 
##                   5 
## [1] ""
## [1] "Summary Statistics: "
##               Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean
## 1 lower middle income 0.29139073 0.35203550 0.37843290 0.36551241
## 2 upper middle income 0.02000000 0.07142857 0.09375000 0.13417857
## 3         high income 0.10714286 0.18181818 0.40000000 0.35482924
##    x.3rd Qu.     x.Max.
## 1 0.39190981 0.41379310
## 2 0.14285714 0.34285714
## 3 0.50000000 0.58518519
## [1] ""
## [1] "N:"
## Other 
##    14 
## [1] ""
## [1] "Summary Statistics: "
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1   Other 0.0200000 0.1160714 0.3171239 0.2790778 0.3961538 0.5851852
## [1] ""
## [1] "Cohends d for effect size of the CRM on geometry_changes_share :"
## 
## Cohen's d
## 
## d estimate: 0.1563367 (negligible)
## 95 percent confidence interval:
##      lower      upper 
## -0.3535038  0.6661772 
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on geometry_changes_share :"
## 
## Cohen's d
## 
## d estimate: 1.339173 (large)
## 95 percent confidence interval:
##     lower     upper 
## 0.6089395 2.0694073 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  geometry_changes_share"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG 0.00000000 0.00000000 0.00000000 0.12160377 0.27417641
## 2     CFM 0.00000000 0.01923077 0.28571429 0.36575092 0.52380952
## 3     CRM 0.00000000 0.00000000 0.02366864 0.06867730 0.07692308
##       x.Max.
## 1 0.70312500
## 2 1.00000000
## 3 0.47058824
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  geometry_changes_share"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG 0.00000000 0.00000000 0.00000000 0.20909931 0.41666667
## 2     CFM 0.00000000 0.02109705 0.03591160 0.05708074 0.06172840
## 3     CRM 0.00000000 0.02857143 0.09183673 0.12321189 0.22222222
##       x.Max.
## 1 1.00000000
## 2 0.16666667
## 3 0.38461538
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE edit_diversity"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame:  one month"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  26  25  57 
## [1] ""
## [1] "Summary Statistics for variable  edit_diversity"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.0000000 0.0000000 0.6365142 0.7222595 1.0778798 2.5942634
## 2     CFM 0.0000000 0.6931472 1.5524150 1.5137655 2.1639557 3.1265855
## 3     CRM 0.0000000 0.0000000 0.5004024 0.5169892 0.7896993 2.4583113
## [1] ""
## [1] ""
## [1] "edit_diversity is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##              CG          CFM
## CFM 0.002835903           NA
## CRM 0.480375719 1.474478e-05
## [1] "Cohends d for effect size of the CFM on edit_diversity :"
## 
## Cohen's d
## 
## d estimate: -0.9523542 (large)
## 95 percent confidence interval:
##      lower      upper 
## -1.5462966 -0.3584117 
## [1] ""
## [1] "N:"
##          low income lower middle income upper middle income 
##                   2                   1                  12 
##         high income 
##                  10 
## [1] ""
## [1] "Summary Statistics: "
##               Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.
## 1          low income 0.8979457 1.2144482 1.5309507 1.5309507 1.8474532
## 2 lower middle income 2.8971441 2.8971441 2.8971441 2.8971441 2.8971441
## 3 upper middle income 0.1934235 0.9972460 1.7760883 1.5890775 2.1032776
## 4         high income 0.0000000 0.6234114 1.2651640 1.2816163 1.7600927
##      x.Max.
## 1 2.1639557
## 2 2.8971441
## 3 2.8009994
## 4 3.1265855
## [1] ""
## [1] "N:"
## Latin  American           Other 
##              15              10 
## [1] ""
## [1] "Summary Statistics: "
##           Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.
## 1 Latin  American 0.6931472 1.1213230 1.5524150 1.5924010 2.0495837
## 2           Other 0.0000000 0.2951092 1.2886082 1.3958123 2.3407128
##      x.Max.
## 1 2.8009994
## 2 3.1265855
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on edit_diversity :"
## 
## Cohen's d
## 
## d estimate: 1.521997 (large)
## 95 percent confidence interval:
##     lower     upper 
## 0.9892365 2.0547567 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  36  14  27 
## [1] ""
## [1] "Summary Statistics for variable  edit_diversity"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.0000000 0.0000000 0.6931472 0.9280879 1.3457079 2.9791941
## 2     CFM 0.6931472 1.0819299 1.4933304 1.5449732 1.5997524 3.0829232
## 3     CRM 0.0000000 0.4970873 0.7667767 0.8811845 1.2552325 2.2242289
## [1] ""
## [1] ""
## [1] "edit_diversity is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] "Pairwise comparison: "
##             CG        CFM
## CFM 0.01211749         NA
## CRM 0.82350010 0.01211749
## [1] "Cohends d for effect size of the CFM on edit_diversity :"
## 
## Cohen's d
## 
## d estimate: -0.7619201 (medium)
## 95 percent confidence interval:
##      lower      upper 
## -1.4134761 -0.1103641 
## [1] ""
## [1] "N:"
##          low income lower middle income upper middle income 
##                   0                   4                   5 
##         high income 
##                   5 
## [1] ""
## [1] "Summary Statistics: "
##               Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.
## 1 lower middle income 0.8587409 1.0021193 1.2426049 1.2112325 1.4517181
## 2 upper middle income 0.7963116 1.1779832 1.4856815 1.3134275 1.5364650
## 3         high income 0.6931472 1.6094379 2.3419943 2.0435115 2.4900548
##      x.Max.
## 1 1.5009793
## 2 1.5706960
## 3 3.0829232
## [1] ""
## [1] "N:"
## Other 
##    14 
## [1] ""
## [1] "Summary Statistics: "
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1   Other 0.6931472 1.0819299 1.4933304 1.5449732 1.5997524 3.0829232
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on edit_diversity :"
## 
## Cohen's d
## 
## d estimate: 1.044023 (large)
## 95 percent confidence interval:
##     lower     upper 
## 0.3382281 1.7498177 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  edit_diversity"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.0000000 0.0000000 1.0397208 1.0363653 1.6434263 2.3826934
## 2     CFM 0.0000000 0.1453885 1.0986123 1.0644432 1.7478681 2.3303471
## 3     CRM 0.0000000 0.2062169 0.4724380 0.5535091 0.7589368 1.6094379
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  edit_diversity"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.0000000 0.6931472 1.0397208 1.0637037 1.6094379 2.3693821
## 2     CFM 0.3235917 0.3984082 0.4067292 0.8168242 0.9954909 1.9599009
## 3     CRM 0.0000000 0.5211689 1.0320802 1.0142600 1.4109406 2.2640138
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE edit_complexity"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame:  one month"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  26  25  57 
## [1] ""
## [1] "Summary Statistics for variable  edit_complexity"
##   Group.1   x.Min. x.1st Qu. x.Median   x.Mean x.3rd Qu.   x.Max.
## 1      CG 1.000000  1.000000 1.000000 1.576923  2.000000 5.000000
## 2     CFM 1.000000  1.000000 2.000000 1.760000  2.000000 3.000000
## 3     CRM 1.000000  2.000000 2.000000 2.192982  2.000000 3.000000
## [1] ""
## [1] ""
## [1] "edit_complexity is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##               CG          CFM
## CFM 7.076192e-02           NA
## CRM 9.401965e-06 0.0006617683
## [1] "Cohends d for effect size of the CRM on edit_complexity :"
## 
## Cohen's d
## 
## d estimate: -0.9619298 (large)
## 95 percent confidence interval:
##      lower      upper 
## -1.4556750 -0.4681847 
## [1] ""
## [1] "N:"
## [1] 57
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on edit_complexity :"
## 
## Cohen's d
## 
## d estimate: -0.9274212 (large)
## 95 percent confidence interval:
##      lower      upper 
## -1.4260837 -0.4287586 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  36  14  27 
## [1] ""
## [1] "Summary Statistics for variable  edit_complexity"
##   Group.1   x.Min. x.1st Qu. x.Median   x.Mean x.3rd Qu.   x.Max.
## 1      CG 1.000000  1.000000 2.000000 1.722222  2.000000 3.000000
## 2     CFM 1.000000  2.000000 2.000000 2.000000  2.000000 3.000000
## 3     CRM 2.000000  2.000000 2.000000 2.148148  2.000000 3.000000
## [1] ""
## [1] ""
## [1] "edit_complexity is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##             CG      CFM
## CFM 0.23265585       NA
## CRM 0.01365483 0.348844
## [1] "Cohends d for effect size of the CRM on edit_complexity :"
## 
## Cohen's d
## 
## d estimate: -0.7324083 (medium)
## 95 percent confidence interval:
##      lower      upper 
## -1.2579410 -0.2068757 
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  edit_complexity"
##   Group.1   x.Min. x.1st Qu. x.Median   x.Mean x.3rd Qu.   x.Max.
## 1      CG 1.000000  1.000000 2.000000 1.606061  2.000000 4.000000
## 2     CFM 1.000000  2.000000 2.000000 2.200000  3.000000 3.000000
## 3     CRM 2.000000  2.000000 2.000000 2.047619  2.000000 3.000000
## [1] ""
## [1] ""
## [1] "edit_complexity is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##              CG       CFM
## CFM 0.142950477        NA
## CRM 0.003201612 0.4064956
## [1] "Cohends d for effect size of the CRM on edit_complexity :"
## 
## Cohen's d
## 
## d estimate: -0.7761269 (medium)
## 95 percent confidence interval:
##      lower      upper 
## -1.3559735 -0.1962802 
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  edit_complexity"
##   Group.1   x.Min. x.1st Qu. x.Median   x.Mean x.3rd Qu.   x.Max.
## 1      CG 1.000000  1.000000 1.000000 1.575758  2.000000 3.000000
## 2     CFM 2.000000  2.000000 2.000000 2.000000  2.000000 2.000000
## 3     CRM 1.000000  2.000000 2.000000 2.000000  2.000000 3.000000
## [1] ""
## [1] ""
## [1] "edit_complexity is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] "Pairwise comparison: "
##             CG CFM
## CFM 0.14940678  NA
## CRM 0.02681752   1
## [1] "Cohends d for effect size of the CRM on edit_complexity :"
## 
## Cohen's d
## 
## d estimate: -0.6830362 (medium)
## 95 percent confidence interval:
##      lower      upper 
## -1.2584993 -0.1075732 
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE quality"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame:  one month"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  26  25  57 
## [1] ""
## [1] "Summary Statistics for variable  quality"
##   Group.1      x.Min.   x.1st Qu.    x.Median      x.Mean   x.3rd Qu.
## 1      CG -0.01020408  0.00000000  0.03571429  0.28269528  0.58695339
## 2     CFM -0.04761905  0.00000000  0.00000000  0.02976652  0.02727273
## 3     CRM -0.04000000  0.02112676  0.25000000  0.35377339  0.57142857
##        x.Max.
## 1  1.00000000
## 2  0.22727273
## 3  1.16666667
## [1] ""
## [1] ""
## [1] "quality is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##             CG          CFM
## CFM 0.08310653           NA
## CRM 0.14946921 1.442479e-05
## [1] "Cohends d for effect size between CRM and CFM on quality :"
## 
## Cohen's d
## 
## d estimate: -1.108065 (large)
## 95 percent confidence interval:
##      lower      upper 
## -1.6155527 -0.6005775 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  36  14  27 
## [1] ""
## [1] "Summary Statistics for variable  quality"
##   Group.1      x.Min.   x.1st Qu.    x.Median      x.Mean   x.3rd Qu.
## 1      CG -0.33727034  0.00000000  0.02147766  0.13142589  0.20555556
## 2     CFM -0.42857143  0.00000000  0.06586586  0.06880971  0.10379464
## 3     CRM -0.31489362  0.01348678  0.11111111  0.23631937  0.48458677
##        x.Max.
## 1  0.70717131
## 2  0.52000000
## 3  1.00000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  quality"
##   Group.1      x.Min.   x.1st Qu.    x.Median      x.Mean   x.3rd Qu.
## 1      CG -0.13333333  0.00000000  0.13602550  0.26277187  0.30434783
## 2     CFM  0.00000000  0.01587302  0.02884615  0.16608669  0.28571429
## 3     CRM -0.11764706  0.04761905  0.28947368  0.33971046  0.63414634
##        x.Max.
## 1  1.50000000
## 2  0.50000000
## 3  1.06896552
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  quality"
##   Group.1      x.Min.   x.1st Qu.    x.Median      x.Mean   x.3rd Qu.
## 1      CG -1.00000000  0.00000000  0.03636364  0.20716708  0.40000000
## 2     CFM  0.21875000  0.36666667  0.90607735  0.67341992  0.91358025
## 3     CRM  0.00000000  0.08219178  0.44444444  0.40565092  0.64117647
##        x.Max.
## 1  2.00000000
## 2  0.96202532
## 3  1.12500000
## [1] ""
## [1] ""
## [1] "quality is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] "Pairwise comparison: "
##             CG       CFM
## CFM 0.03564029        NA
## CRM 0.03564029 0.1342881
## [1] "Cohends d for effect size of the CFM on quality :"
## 
## Cohen's d
## 
## d estimate: -0.9698925 (large)
## 95 percent confidence interval:
##       lower       upper 
## -1.96898473  0.02919966 
## [1] ""
## [1] "N:"
##          low income lower middle income upper middle income 
##                   0                   0                   3 
##         high income 
##                   2 
## [1] ""
## [1] "Summary Statistics: "
##               Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.
## 1 upper middle income 0.2187500 0.5624137 0.9060773 0.6956176 0.9340513
## 2         high income 0.3666667 0.5033951 0.6401235 0.6401235 0.7768519
##      x.Max.
## 1 0.9620253
## 2 0.9135802
## [1] ""
## [1] "N:"
## Other 
##     5 
## [1] ""
## [1] "Summary Statistics: "
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1   Other 0.2187500 0.3666667 0.9060773 0.6734199 0.9135802 0.9620253
## [1] ""
## [1] "Cohends d for effect size of the CRM on quality :"
## 
## Cohen's d
## 
## d estimate: -0.4488552 (small)
## 95 percent confidence interval:
##      lower      upper 
## -1.0156665  0.1179561 
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE element_density"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "

## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  36  14  27 
## [1] ""
## [1] "Summary Statistics for variable  element_density"
##   Group.1      x.Min.   x.1st Qu.    x.Median      x.Mean   x.3rd Qu.
## 1      CG 0.000000000 0.001583661 0.028424760 0.123349977 0.144928829
## 2     CFM 0.000000000 0.000000000 0.007579649 0.056060249 0.024424192
## 3     CRM 0.000000000 0.004821217 0.013195166 0.165682608 0.090116001
##        x.Max.
## 1 0.686575563
## 2 0.436351247
## 3 1.591428030
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  element_density"
##   Group.1       x.Min.    x.1st Qu.     x.Median       x.Mean    x.3rd Qu.
## 1      CG 0.0000000000 0.0007672222 0.0176057247 0.2094394577 0.1532382921
## 2     CFM 0.0033924164 0.0440480009 0.1080512362 0.5880473506 0.6031819993
## 3     CRM 0.0000000000 0.0000280076 0.0009802658 0.1190995113 0.0199959197
##         x.Max.
## 1 2.4018896879
## 2 2.1815631003
## 3 1.1588027348
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  element_density"
##   Group.1       x.Min.    x.1st Qu.     x.Median       x.Mean    x.3rd Qu.
## 1      CG 0.0000000000 0.0071655290 0.0455573286 0.1102166689 0.1138113441
## 2     CFM 0.0012594434 0.0183221637 0.1017061091 0.3171414107 0.7126757977
## 3     CRM 0.0000000000 0.0008371309 0.0481353713 0.4711269581 0.3174534647
##         x.Max.
## 1 0.7515843768
## 2 0.7517435396
## 3 3.1144483034
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE tag_density"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  36  14  27 
## [1] ""
## [1] "Summary Statistics for variable  tag_density"
##   Group.1   x.Min. x.1st Qu. x.Median   x.Mean x.3rd Qu.   x.Max.
## 1      CG 0.000000  1.371429 2.540794 2.277550  3.261425 5.009355
## 2     CFM 0.000000  0.000000 1.407382 1.448019  2.637216 4.916667
## 3     CRM 0.000000  1.049143 1.159443 1.482301  1.904762 5.831715
## [1] ""
## [1] ""
## [1] "tag_density is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] "Pairwise comparison: "
##             CG       CFM
## CFM 0.13564758        NA
## CRM 0.02394484 0.9669319
## [1] "Cohends d for effect size of the CRM on tag_density :"
## 
## Cohen's d
## 
## d estimate: 0.5832377 (medium)
## 95 percent confidence interval:
##      lower      upper 
## 0.06366428 1.10281113 
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] "Analyses of the effect of the distance to the Region mapped during the event for CRM"

## NULL
## NULL
## 
## Call:
## lm(formula = variable2 ~ classes2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7591 -0.2865 -0.1843  0.1029  3.0726 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)   7.0682     2.5236   2.801  0.00969 **
## classes2     -1.4364     0.6468  -2.221  0.03566 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.056 on 25 degrees of freedom
## Multiple R-squared:  0.1648, Adjusted R-squared:  0.1314 
## F-statistic: 4.932 on 1 and 25 DF,  p-value: 0.03566
## 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  tag_density"
##   Group.1   x.Min. x.1st Qu. x.Median   x.Mean x.3rd Qu.   x.Max.
## 1      CG 0.000000  1.480040 2.131967 2.328615  3.173203 6.436430
## 2     CFM 1.955952  2.291667 2.643478 2.582108  2.785111 3.234332
## 3     CRM 0.000000  1.166667 1.609756 1.570207  2.000000 3.333333
## [1] ""
## [1] ""
## [1] "tag_density is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##             CG        CFM
## CFM 0.63320899         NA
## CRM 0.08875075 0.03999103
## [1] "Cohends d for effect size between CRM and CFM on tag_density :"
## 
## Cohen's d
## 
## d estimate: 1.367256 (large)
## 95 percent confidence interval:
##     lower     upper 
## 0.2682058 2.4663066 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  tag_density"
##   Group.1   x.Min. x.1st Qu. x.Median   x.Mean x.3rd Qu.   x.Max.
## 1      CG 0.000000  1.784946 2.222222 2.341982  3.087719 4.960573
## 2     CFM 1.090200  1.309353 1.612167 1.717186  1.862312 2.711896
## 3     CRM 0.000000  1.000000 1.051366 1.118664  1.285307 2.826444
## [1] ""
## [1] ""
## [1] "tag_density is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] "Pairwise comparison: "
##               CG        CFM
## CFM 0.1596325086         NA
## CRM 0.0007992705 0.05578094
## [1] "Cohends d for effect size of the CRM on tag_density :"
## 
## Cohen's d
## 
## d estimate: 1.023137 (large)
## 95 percent confidence interval:
##    lower    upper 
## 0.429174 1.617101 
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE user_density"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "

## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  36  14  27 
## [1] ""
## [1] "Summary Statistics for variable  user_density"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG  0.000000  1.239081  2.158742  1.893394  2.747223  3.500000
## 2     CFM  0.000000  0.000000  1.619197  1.336162  2.358938  3.176471
## 3     CRM  0.000000  1.327098  1.600619  2.198787  2.236111 12.384615
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  user_density"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG  0.000000  1.250000  1.899071  1.767267  2.558030  3.713235
## 2     CFM  1.339985  1.401042  1.901864  1.825766  2.060870  2.425068
## 3     CRM  0.000000  1.692308  2.500287  3.721175  3.804878 20.000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  user_density"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG  0.000000  1.688528  2.161677  1.891375  2.504854  4.128655
## 2     CFM  1.140312  1.931559  2.048327  2.046552  2.112563  3.000000
## 3     CRM  0.000000  1.728142  2.006776  2.423095  2.360269 13.000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE area_diversity"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  36  14  27 
## [1] ""
## [1] "Summary Statistics for variable  area_diversity"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.0000000 0.6567309 1.1802398 1.0234693 1.5276886 2.0453477
## 2     CFM 0.0000000 0.0000000 0.5554145 0.5814001 0.8160916 2.0937048
## 3     CRM 0.0000000 0.4588719 0.6450062 0.7192085 1.0820974 1.6933732
## [1] ""
## [1] ""
## [1] "area_diversity is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##             CG       CFM
## CFM 0.04710703        NA
## CRM 0.04710703 0.3255645
## [1] "Cohends d for effect size of the CFM on area_diversity :"
## 
## Cohen's d
## 
## d estimate: 0.6687833 (medium)
## 95 percent confidence interval:
##      lower      upper 
## 0.02137435 1.31619229 
## [1] ""
## [1] "N:"
##          low income lower middle income upper middle income 
##                   0                   4                   5 
##         high income 
##                   5 
## [1] ""
## [1] "Summary Statistics: "
##               Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.
## 1 lower middle income 0.0000000 0.0000000 0.0000000 0.1437170 0.1437170
## 2 upper middle income 0.0000000 0.6726303 0.8395389 0.8378637 1.3249397
## 3         high income 0.0000000 0.0000000 0.5359610 0.6750831 0.7457497
##      x.Max.
## 1 0.5748679
## 2 1.3522094
## 3 2.0937048
## [1] ""
## [1] "N:"
## Other 
##    14 
## [1] ""
## [1] "Summary Statistics: "
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1   Other 0.0000000 0.0000000 0.5554145 0.5814001 0.8160916 2.0937048
## [1] ""
## [1] "Cohends d for effect size of the CRM on area_diversity :"
## 
## Cohen's d
## 
## d estimate: 0.5146849 (medium)
## 95 percent confidence interval:
##        lower        upper 
## -0.002584912  1.031954706 
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  area_diversity"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.0000000 0.7697337 1.4388325 1.1185299 1.6396943 1.8865400
## 2     CFM 0.6128310 0.7188128 1.1627699 1.1438574 1.4515277 1.7733455
## 3     CRM 0.0000000 0.0000000 0.7286722 0.7510325 1.1358786 1.6890652
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  area_diversity"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.0000000 0.9441687 1.3917973 1.1781853 1.6785351 1.9820004
## 2     CFM 0.2530907 0.8364142 1.0535875 0.9272660 1.1975143 1.2957235
## 3     CRM 0.0000000 0.2416494 0.6737367 0.6747114 0.9002561 1.8861502
## [1] ""
## [1] ""
## [1] "area_diversity is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] "Pairwise comparison: "
##             CG       CFM
## CFM 0.21483191        NA
## CRM 0.01103815 0.2148319
## [1] "Cohends d for effect size of the CRM on area_diversity :"
## 
## Cohen's d
## 
## d estimate: 0.8242096 (large)
## 95 percent confidence interval:
##     lower     upper 
## 0.2418943 1.4065248 
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE economic_distance"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame:  one month"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  26  25  57 
## [1] ""
## [1] "Summary Statistics for variable  economic_distance"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG 0.00000000 0.00000000 0.00000000 0.11888112 0.00000000
## 2     CFM 0.00000000 0.00000000 0.00000000 0.04198675 0.00000000
## 3     CRM 0.00000000 1.00000000 1.00000000 0.92424405 1.00000000
##       x.Max.
## 1 1.00000000
## 2 1.00000000
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "economic_distance is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] "Pairwise comparison: "
##               CG          CFM
## CFM 3.995521e-01           NA
## CRM 5.842675e-13 2.866003e-14
## [1] "Cohends d for effect size of the CRM on economic_distance :"
## 
## Cohen's d
## 
## d estimate: -2.86635 (large)
## 95 percent confidence interval:
##     lower     upper 
## -3.512613 -2.220087 
## [1] ""
## [1] "N:"
## [1] 57
## [1] ""
## [1] "Analyses of the effect of the distance to the Region mapped during the event for CRM"

## NULL
## NULL
## 
## Call:
## lm(formula = variable2 ~ classes2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.97823  0.02177  0.02177  0.02177  0.26296 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.01346    0.18682   0.072    0.943    
## classes2     0.24119    0.04888   4.934 7.83e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2175 on 55 degrees of freedom
## Multiple R-squared:  0.3068, Adjusted R-squared:  0.2942 
## F-statistic: 24.35 on 1 and 55 DF,  p-value: 7.827e-06
## 
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on economic_distance :"
## 
## Cohen's d
## 
## d estimate: -3.635228 (large)
## 95 percent confidence interval:
##     lower     upper 
## -4.374831 -2.895624 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  36  14  27 
## [1] ""
## [1] "Summary Statistics for variable  economic_distance"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG 0.00000000 0.00000000 0.00000000 0.11605620 0.00000000
## 2     CFM 0.00000000 0.00000000 0.00000000 0.01068616 0.00000000
## 3     CRM 0.00000000 1.00000000 1.00000000 0.96290817 1.00000000
##       x.Max.
## 1 1.00000000
## 2 0.14960630
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "economic_distance is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##               CG          CFM
## CFM 3.611125e-01           NA
## CRM 3.512493e-11 9.439673e-09
## [1] "Cohends d for effect size of the CRM on economic_distance :"
## 
## Cohen's d
## 
## d estimate: -3.284846 (large)
## 95 percent confidence interval:
##     lower     upper 
## -4.060462 -2.509231 
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on economic_distance :"
## 
## Cohen's d
## 
## d estimate: -5.995877 (large)
## 95 percent confidence interval:
##     lower     upper 
## -7.491693 -4.500060 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  economic_distance"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.0000000 0.0000000 0.0000000 0.1017330 0.0000000 1.0000000
## 2     CFM 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 3     CRM 0.0000000 1.0000000 1.0000000 0.9042432 1.0000000 1.0000000
## [1] ""
## [1] ""
## [1] "economic_distance is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##               CG          CFM
## CFM 2.775890e-01           NA
## CRM 6.665277e-08 0.0003935208
## [1] "Cohends d for effect size of the CRM on economic_distance :"
## 
## Cohen's d
## 
## d estimate: -2.85522 (large)
## 95 percent confidence interval:
##     lower     upper 
## -3.641165 -2.069274 
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on economic_distance :"
## 
## Cohen's d
## 
## d estimate: -3.294956 (large)
## 95 percent confidence interval:
##     lower     upper 
## -4.689276 -1.900637 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  economic_distance"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG 0.00000000 0.00000000 0.00000000 0.06632823 0.00000000
## 2     CFM 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 3     CRM 0.00000000 1.00000000 1.00000000 0.85815160 1.00000000
##       x.Max.
## 1 1.00000000
## 2 0.00000000
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "economic_distance is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] "Pairwise comparison: "
##               CG          CFM
## CFM 3.239594e-01           NA
## CRM 4.379284e-09 0.0003171088
## [1] "Cohends d for effect size of the CRM on economic_distance :"
## 
## Cohen's d
## 
## d estimate: -3.158135 (large)
## 95 percent confidence interval:
##     lower     upper 
## -3.986158 -2.330111 
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on economic_distance :"
## 
## Cohen's d
## 
## d estimate: -2.957872 (large)
## 95 percent confidence interval:
##     lower     upper 
## -4.288838 -1.626907 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE cultural_distance"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame:  one month"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  26  25  57 
## [1] ""
## [1] "Summary Statistics for variable  cultural_distance"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG 0.00000000 0.00000000 0.00000000 0.08041958 0.00000000
## 2     CFM 0.00000000 0.00000000 0.00000000 0.04198675 0.00000000
## 3     CRM 0.00000000 1.00000000 1.00000000 0.94178790 1.00000000
##       x.Max.
## 1 1.00000000
## 2 1.00000000
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "cultural_distance is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] "Pairwise comparison: "
##               CG          CFM
## CFM 6.611596e-01           NA
## CRM 1.173131e-14 4.730205e-15
## [1] "Cohends d for effect size of the CRM on cultural_distance :"
## 
## Cohen's d
## 
## d estimate: -3.565341 (large)
## 95 percent confidence interval:
##     lower     upper 
## -4.289821 -2.840861 
## [1] ""
## [1] "N:"
## [1] 57
## [1] ""
## [1] "Analyses of the effect of the distance to the Region mapped during the event for CRM"

## NULL
## NULL
## 
## Call:
## lm(formula = variable2 ~ classes2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.7458  0.0017  0.0017  0.0017  0.2542 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.01169    0.14766  -0.079    0.937    
## classes2     0.25250    0.03864   6.535 2.17e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1719 on 55 degrees of freedom
## Multiple R-squared:  0.4371, Adjusted R-squared:  0.4269 
## F-statistic: 42.71 on 1 and 55 DF,  p-value: 2.171e-08
## 
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on cultural_distance :"
## 
## Cohen's d
## 
## d estimate: -4.103755 (large)
## 95 percent confidence interval:
##     lower     upper 
## -4.900356 -3.307153 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  36  14  27 
## [1] ""
## [1] "Summary Statistics for variable  cultural_distance"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG 0.00000000 0.00000000 0.00000000 0.10461899 0.00000000
## 2     CFM 0.00000000 0.00000000 0.00000000 0.01068616 0.00000000
## 3     CRM 0.00000000 1.00000000 1.00000000 0.96290817 1.00000000
##       x.Max.
## 1 1.00000000
## 2 0.14960630
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "cultural_distance is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##               CG          CFM
## CFM 4.669727e-01           NA
## CRM 2.333447e-11 9.439673e-09
## [1] "Cohends d for effect size of the CRM on cultural_distance :"
## 
## Cohen's d
## 
## d estimate: -3.525514 (large)
## 95 percent confidence interval:
##     lower     upper 
## -4.333965 -2.717063 
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on cultural_distance :"
## 
## Cohen's d
## 
## d estimate: -5.995877 (large)
## 95 percent confidence interval:
##     lower     upper 
## -7.491693 -4.500060 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  cultural_distance"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG 0.00000000 0.00000000 0.00000000 0.09887073 0.00000000
## 2     CFM 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 3     CRM 0.00000000 1.00000000 1.00000000 0.90424316 1.00000000
##       x.Max.
## 1 1.00000000
## 2 0.00000000
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "cultural_distance is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##               CG          CFM
## CFM 2.374395e-01           NA
## CRM 9.847510e-08 0.0003935208
## [1] "Cohends d for effect size of the CRM on cultural_distance :"
## 
## Cohen's d
## 
## d estimate: -2.966147 (large)
## 95 percent confidence interval:
##     lower     upper 
## -3.767263 -2.165032 
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on cultural_distance :"
## 
## Cohen's d
## 
## d estimate: -3.294956 (large)
## 95 percent confidence interval:
##     lower     upper 
## -4.689276 -1.900637 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  cultural_distance"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG 0.00000000 0.00000000 0.00000000 0.02594502 0.00000000
## 2     CFM 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 3     CRM 0.00000000 1.00000000 1.00000000 0.87929282 1.00000000
##       x.Max.
## 1 0.34782609
## 2 0.00000000
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "cultural_distance is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] "Pairwise comparison: "
##               CG          CFM
## CFM 3.778002e-01           NA
## CRM 7.276532e-10 0.0003171088
## [1] "Cohends d for effect size of the CRM on cultural_distance :"
## 
## Cohen's d
## 
## d estimate: -4.216276 (large)
## 95 percent confidence interval:
##     lower     upper 
## -5.204483 -3.228068 
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on cultural_distance :"
## 
## Cohen's d
## 
## d estimate: -3.096082 (large)
## 95 percent confidence interval:
##     lower     upper 
## -4.452552 -1.739613 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE population_density_distance"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame:  one month"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  26  25  57 
## [1] ""
## [1] "Summary Statistics for variable  population_density_distance"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG 0.00000000 0.00000000 0.00000000 0.29423766 0.53729839
## 2     CFM 0.00000000 0.00000000 0.00000000 0.08248284 0.00000000
## 3     CRM 0.00000000 1.00000000 1.00000000 0.86210803 1.00000000
##       x.Max.
## 1 1.00000000
## 2 1.00000000
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "population_density_distance is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] "Pairwise comparison: "
##               CG          CFM
## CFM 1.857187e-02           NA
## CRM 1.689396e-07 6.918854e-11
## [1] "Cohends d for effect size of the CFM on population_density_distance :"
## 
## Cohen's d
## 
## d estimate: 0.5927155 (medium)
## 95 percent confidence interval:
##      lower      upper 
## 0.01759147 1.16783955 
## [1] ""
## [1] "N:"
##          low income lower middle income upper middle income 
##                   2                   1                  12 
##         high income 
##                  10 
## [1] ""
## [1] "Summary Statistics: "
##               Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean
## 1          low income 0.00000000 0.00000000 0.00000000 0.00000000
## 2 lower middle income 0.00000000 0.00000000 0.00000000 0.00000000
## 3 upper middle income 0.00000000 0.00000000 0.00000000 0.08409091
## 4         high income 0.00000000 0.00000000 0.00000000 0.10529801
##    x.3rd Qu.     x.Max.
## 1 0.00000000 0.00000000
## 2 0.00000000 0.00000000
## 3 0.00000000 1.00000000
## 4 0.00000000 1.00000000
## [1] ""
## [1] "N:"
## Latin  American           Other 
##              15              10 
## [1] ""
## [1] "Summary Statistics: "
##           Group.1      x.Min.   x.1st Qu.    x.Median      x.Mean
## 1 Latin  American 0.000000000 0.000000000 0.000000000 0.133939394
## 2           Other 0.000000000 0.000000000 0.000000000 0.005298013
##     x.3rd Qu.      x.Max.
## 1 0.000000000 1.000000000
## 2 0.000000000 0.052980132
## [1] ""
## [1] "Cohends d for effect size of the CRM on population_density_distance :"
## 
## Cohen's d
## 
## d estimate: -1.624276 (large)
## 95 percent confidence interval:
##     lower     upper 
## -2.157789 -1.090763 
## [1] ""
## [1] "N:"
## [1] 57
## [1] ""
## [1] "Analyses of the effect of the distance to the Region mapped during the event for CRM"

## NULL
## NULL
## 
## Call:
## lm(formula = variable2 ~ classes2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.60677  0.06426  0.06426  0.06426  0.62282 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.38014    0.21156  -1.797   0.0779 .  
## classes2     0.32897    0.05536   5.943 1.99e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2463 on 55 degrees of freedom
## Multiple R-squared:  0.391,  Adjusted R-squared:   0.38 
## F-statistic: 35.32 on 1 and 55 DF,  p-value: 1.989e-07
## 
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on population_density_distance :"
## 
## Cohen's d
## 
## d estimate: -2.578756 (large)
## 95 percent confidence interval:
##     lower     upper 
## -3.202038 -1.955474 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  36  14  27 
## [1] ""
## [1] "Summary Statistics for variable  population_density_distance"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG 0.00000000 0.00000000 0.00000000 0.27473688 0.50431034
## 2     CFM 0.00000000 0.00000000 0.00000000 0.09448718 0.11220472
## 3     CRM 0.00000000 1.00000000 1.00000000 0.91504830 1.00000000
##       x.Max.
## 1 1.00000000
## 2 0.81250000
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "population_density_distance is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##               CG         CFM
## CFM 1.657085e-01          NA
## CRM 1.482570e-07 1.48257e-07
## [1] "Cohends d for effect size of the CRM on population_density_distance :"
## 
## Cohen's d
## 
## d estimate: -1.909822 (large)
## 95 percent confidence interval:
##     lower     upper 
## -2.522120 -1.297523 
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on population_density_distance :"
## 
## Cohen's d
## 
## d estimate: -3.425136 (large)
## 95 percent confidence interval:
##     lower     upper 
## -4.439578 -2.410694 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  population_density_distance"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.0000000 0.0000000 0.0000000 0.3018222 0.7582978 1.0000000
## 2     CFM 0.0000000 0.0000000 0.0000000 0.2000000 0.0000000 1.0000000
## 3     CRM 0.0000000 0.4856213 1.0000000 0.7383535 1.0000000 1.0000000
## [1] ""
## [1] ""
## [1] "population_density_distance is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##             CG        CFM
## CFM 0.43985090         NA
## CRM 0.00297149 0.05037693
## [1] "Cohends d for effect size of the CRM on population_density_distance :"
## 
## Cohen's d
## 
## d estimate: -1.040683 (large)
## 95 percent confidence interval:
##      lower      upper 
## -1.6357821 -0.4455847 
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  population_density_distance"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.0000000 0.0000000 0.0000000 0.2780406 0.4347826 1.0000000
## 2     CFM 0.0000000 0.0000000 0.0000000 0.1719056 0.4037267 0.4558011
## 3     CRM 0.0000000 0.4415954 0.7142857 0.6494678 1.0000000 1.0000000
## [1] ""
## [1] ""
## [1] "population_density_distance is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] "Pairwise comparison: "
##              CG        CFM
## CFM 0.690625318         NA
## CRM 0.004457559 0.02201713
## [1] "Cohends d for effect size of the CRM on population_density_distance :"
## 
## Cohen's d
## 
## d estimate: -0.9535705 (large)
## 95 percent confidence interval:
##     lower     upper 
## -1.543202 -0.363939 
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on population_density_distance :"
## 
## Cohen's d
## 
## d estimate: -1.268177 (large)
## 95 percent confidence interval:
##      lower      upper 
## -2.3574530 -0.1789012 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE physical_geography_distance"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "
## [1] ""
## [1] ""
## [1] "Time frame:  one month"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  26  25  57 
## [1] ""
## [1] "Summary Statistics for variable  physical_geography_distance"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.0000000 0.0000000 0.0000000 0.2557346 0.3645833 1.0000000
## 2     CFM 0.0000000 0.0000000 0.0000000 0.1272176 0.0000000 1.0000000
## 3     CRM 0.0000000 1.0000000 1.0000000 0.9242440 1.0000000 1.0000000
## [1] ""
## [1] ""
## [1] "physical_geography_distance is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] "Pairwise comparison: "
##               CG          CFM
## CFM 3.364231e-01           NA
## CRM 8.554529e-11 7.064269e-13
## [1] "Cohends d for effect size of the CRM on physical_geography_distance :"
## 
## Cohen's d
## 
## d estimate: -2.109782 (large)
## 95 percent confidence interval:
##     lower     upper 
## -2.682382 -1.537182 
## [1] ""
## [1] "N:"
## [1] 57
## [1] ""
## [1] "Analyses of the effect of the distance to the Region mapped during the event for CRM"

## NULL
## NULL
## 
## Call:
## lm(formula = variable2 ~ classes2)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.97823  0.02177  0.02177  0.02177  0.26296 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.01346    0.18682   0.072    0.943    
## classes2     0.24119    0.04888   4.934 7.83e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2175 on 55 degrees of freedom
## Multiple R-squared:  0.3068, Adjusted R-squared:  0.2942 
## F-statistic: 24.35 on 1 and 55 DF,  p-value: 7.827e-06
## 
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on physical_geography_distance :"
## 
## Cohen's d
## 
## d estimate: -2.937302 (large)
## 95 percent confidence interval:
##     lower     upper 
## -3.597788 -2.276817 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  36  14  27 
## [1] ""
## [1] "Summary Statistics for variable  physical_geography_distance"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.0000000 0.0000000 0.0000000 0.1464917 0.1551724 1.0000000
## 2     CFM 0.0000000 0.0000000 0.0000000 0.1810848 0.1875000 1.0000000
## 3     CRM 0.3493450 1.0000000 1.0000000 0.9758469 1.0000000 1.0000000
## [1] ""
## [1] ""
## [1] "physical_geography_distance is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##               CG         CFM
## CFM 9.574789e-01          NA
## CRM 3.578816e-11 4.76409e-08
## [1] "Cohends d for effect size of the CRM on physical_geography_distance :"
## 
## Cohen's d
## 
## d estimate: -3.524348 (large)
## 95 percent confidence interval:
##     lower     upper 
## -4.332637 -2.716058 
## [1] ""
## [1] "N:"
## [1] 27
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on physical_geography_distance :"
## 
## Cohen's d
## 
## d estimate: -3.62436 (large)
## 95 percent confidence interval:
##     lower     upper 
## -4.672771 -2.575949 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  physical_geography_distance"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG 0.00000000 0.00000000 0.00000000 0.14461681 0.10000000
## 2     CFM 0.00000000 0.00000000 0.00000000 0.01509434 0.00000000
## 3     CRM 0.00000000 1.00000000 1.00000000 0.92825938 1.00000000
##       x.Max.
## 1 1.00000000
## 2 0.07547170
## 3 1.00000000
## [1] ""
## [1] ""
## [1] "physical_geography_distance is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties
## [1] "Pairwise comparison: "
##               CG          CFM
## CFM 5.176129e-01           NA
## CRM 3.570673e-08 0.0002426442
## [1] "Cohends d for effect size of the CRM on physical_geography_distance :"
## 
## Cohen's d
## 
## d estimate: -2.857471 (large)
## 95 percent confidence interval:
##     lower     upper 
## -3.643721 -2.071221 
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on physical_geography_distance :"
## 
## Cohen's d
## 
## d estimate: -4.177999 (large)
## 95 percent confidence interval:
##     lower     upper 
## -5.754289 -2.601708 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  physical_geography_distance"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.0000000 0.0000000 0.0000000 0.1524312 0.1666667 1.0000000
## 2     CFM 0.0000000 0.0000000 0.0000000 0.1123467 0.1059322 0.4558011
## 3     CRM 0.0000000 1.0000000 1.0000000 0.8784061 1.0000000 1.0000000
## [1] ""
## [1] ""
## [1] "physical_geography_distance is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot
## compute exact p-value with ties

## [1] "Pairwise comparison: "
##               CG         CFM
## CFM 8.545558e-01          NA
## CRM 3.345239e-07 0.001182783
## [1] "Cohends d for effect size of the CRM on physical_geography_distance :"
## 
## Cohen's d
## 
## d estimate: -2.395567 (large)
## 95 percent confidence interval:
##     lower     upper 
## -3.122014 -1.669121 
## [1] ""
## [1] "N:"
## [1] 21
## [1] ""
## [1] "Cohends d for effect size between CRM and CFM on physical_geography_distance :"
## 
## Cohen's d
## 
## d estimate: -2.566349 (large)
## 95 percent confidence interval:
##     lower     upper 
## -3.829002 -1.303695 
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE comment_size"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "

## [1] ""
## [1] ""
## [1] "Time frame:  one month"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  26  25  57 
## [1] ""
## [1] "Summary Statistics for variable  comment_size"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG  0.0000000  0.7500000  2.0000000  3.1115832  3.4583333
## 2     CFM  0.1475410  1.1702128  1.6666667  2.6538324  3.3333333
## 3     CRM  0.0000000  0.6153846  3.0000000  3.3741301  5.5000000
##       x.Max.
## 1 18.0000000
## 2 10.0000000
## 3 10.0000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  36  14  27 
## [1] ""
## [1] "Summary Statistics for variable  comment_size"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG  0.0000000  0.7443182  1.9275362  2.1938107  3.0000000
## 2     CFM  0.3333333  0.9226190  2.2767857  4.8841270  5.5000000
## 3     CRM  0.2307692  0.5357143  1.1500000  1.6592537  2.0000000
##       x.Max.
## 1  7.0000000
## 2 21.0000000
## 3  5.5000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  comment_size"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG  0.3333333  1.0000000  2.0000000  2.2777637  3.0000000
## 2     CFM  1.0000000  1.1666667  1.5000000  2.4333333  3.0000000
## 3     CRM  0.1250000  0.8461538  1.9275362  3.0044324  4.5000000
##       x.Max.
## 1  7.0000000
## 2  5.5000000
## 3 10.0000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  33   5  21 
## [1] ""
## [1] "Summary Statistics for variable  comment_size"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG 0.1666667 0.7142857 1.5714286 2.2260519 3.0000000 8.8000000
## 2     CFM 0.4090909 1.0000000 1.0000000 1.0212121 1.3333333 1.3636364
## 3     CRM 0.1492537 0.4000000 1.0000000 1.3776457 1.6511628 8.0000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE discussion_size"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "

## [1] ""
## [1] ""
## [1] "Time frame:  one month"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  75  33  85 
## [1] ""
## [1] "Summary Statistics for variable  discussion_size"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG 0.00000000 0.00000000 0.00000000 0.02666667 0.00000000
## 2     CFM 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 3     CRM 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
##       x.Max.
## 1 2.00000000
## 2 0.00000000
## 3 0.00000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  75  33  85 
## [1] ""
## [1] "Summary Statistics for variable  discussion_size"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG  0.0000000  0.0000000  0.0000000  0.6333333  0.0000000
## 2     CFM  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
## 3     CRM  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
##       x.Max.
## 1 47.5000000
## 2  0.0000000
## 3  0.0000000
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  75  33  85 
## [1] ""
## [1] "Summary Statistics for variable  discussion_size"
##   Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1      CG   0.00      0.00     0.00   0.12      0.00   9.00
## 2     CFM   0.00      0.00     0.00   0.00      0.00   0.00
## 3     CRM   0.00      0.00     0.00   0.00      0.00   0.00
## [1] ""
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## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  75  33  85 
## [1] ""
## [1] "Summary Statistics for variable  discussion_size"
##   Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1      CG      0         0        0      0         0      0
## 2     CFM      0         0        0      0         0      0
## 3     CRM      0         0        0      0         0      0
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## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "========================================================================"
## [1] "RESULTS FOR VARIABLE notes_size"
## [1] "========================================================================"
## [1] ""
## [1] ""

## [1] ""

## [1] ""
## [1] "Boxplot on contributors only: "

## [1] ""
## [1] ""
## [1] "Time frame:  one month"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  75  33  85 
## [1] ""
## [1] "Summary Statistics for variable  notes_size"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG  0.000000  0.000000  0.000000  1.268889  0.000000 46.000000
## 2     CFM  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000
## 3     CRM  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000
## [1] ""
## [1] ""
## [1] "notes_size is significantly influenced by an event for one month"
## [1] ""
## [1] ""
## [1] "Pairwise comparison: "
##             CG CFM
## CFM 0.18182503  NA
## CRM 0.06422603 NaN
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  six months"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  75  33  85 
## [1] ""
## [1] "Summary Statistics for variable  notes_size"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG  0.000000  0.000000  0.000000  3.522667  0.000000 56.000000
## 2     CFM  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000
## 3     CRM  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000
## [1] ""
## [1] ""
## [1] "notes_size is significantly influenced by an event for six months"
## [1] ""
## [1] ""
## [1] "Pairwise comparison: "
##              CG CFM
## CFM 0.029119631  NA
## CRM 0.001081845 NaN
## [1] "Cohends d for effect size of the CFM on notes_size :"
## 
## Cohen's d
## 
## d estimate: 0.3658006 (small)
## 95 percent confidence interval:
##       lower       upper 
## -0.05127991  0.78288105 
## [1] ""
## [1] "N:"
##          low income lower middle income upper middle income 
##                   2                   4                  13 
##         high income 
##                  14 
## [1] ""
## [1] "Summary Statistics: "
##               Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1          low income      0         0        0      0         0      0
## 2 lower middle income      0         0        0      0         0      0
## 3 upper middle income      0         0        0      0         0      0
## 4         high income      0         0        0      0         0      0
## [1] ""
## [1] "N:"
## Latin  American           Other 
##              20              13 
## [1] ""
## [1] "Summary Statistics: "
##           Group.1 x.Min. x.1st Qu. x.Median x.Mean x.3rd Qu. x.Max.
## 1 Latin  American      0         0        0      0         0      0
## 2           Other      0         0        0      0         0      0
## [1] ""
## [1] "Cohends d for effect size of the CRM on notes_size :"
## 
## Cohen's d
## 
## d estimate: 0.4466013 (small)
## 95 percent confidence interval:
##     lower     upper 
## 0.1298386 0.7633640 
## [1] ""
## [1] "N:"
## [1] 85
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  one year"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  75  33  85 
## [1] ""
## [1] "Summary Statistics for variable  notes_size"
##   Group.1     x.Min.  x.1st Qu.   x.Median     x.Mean  x.3rd Qu.
## 1      CG  0.0000000  0.0000000  0.0000000  2.9941799  0.0000000
## 2     CFM  0.0000000  0.0000000  0.0000000  0.1717172  0.0000000
## 3     CRM  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
##       x.Max.
## 1 47.0000000
## 2  5.6666667
## 3  0.0000000
## [1] ""
## [1] ""
## [1] "notes_size is significantly influenced by an event for one year"
## [1] ""
## [1] ""
## [1] "Pairwise comparison: "
##              CG       CFM
## CFM 0.111465353        NA
## CRM 0.000816572 0.1127313
## [1] "Cohends d for effect size of the CRM on notes_size :"
## 
## Cohen's d
## 
## d estimate: 0.4647163 (small)
## 95 percent confidence interval:
##     lower     upper 
## 0.1476361 0.7817965 
## [1] ""
## [1] "N:"
## [1] 85
## [1] ""
## [1] ""
## [1] ""
## [1] "--------------------------------------------------------------------------------"
## [1] ""
## [1] ""
## [1] ""
## [1] "Time frame:  two years"
## [1] ""
## [1] "N:"
##  CG CFM CRM 
##  75  33  85 
## [1] ""
## [1] "Summary Statistics for variable  notes_size"
##   Group.1    x.Min. x.1st Qu.  x.Median    x.Mean x.3rd Qu.    x.Max.
## 1      CG  0.000000  0.000000  0.000000  3.007778  0.000000 54.000000
## 2     CFM  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000
## 3     CRM  0.000000  0.000000  0.000000  0.800000  0.000000 30.000000
## [1] ""
## [1] ""
## [1] "notes_size is significantly influenced by an event for two years"
## [1] ""
## [1] ""
## [1] "Pairwise comparison: "
##            CG       CFM
## CFM 0.1132692        NA
## CRM 0.1132692 0.2812757
## [1] ""
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## [1] "--------------------------------------------------------------------------------"
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